Multiple password interference in text passwords and click-based graphical passwords
Proceedings of the 16th ACM conference on Computer and communications security
Purely automated attacks on passpoints-style graphical passwords
IEEE Transactions on Information Forensics and Security
Tag association based graphical password using image feature matching
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Graphical passwords: Learning from the first twelve years
ACM Computing Surveys (CSUR)
On automated image choice for secure and usable graphical passwords
Proceedings of the 28th Annual Computer Security Applications Conference
Security implications of password discretization for click-based graphical passwords
Proceedings of the 22nd international conference on World Wide Web
Quantifying the security of graphical passwords: the case of android unlock patterns
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
On the security of picture gesture authentication
SEC'13 Proceedings of the 22nd USENIX conference on Security
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We present and evaluate various methods for purely automated attacks against click-based graphical passwords. Our purely automated methods combine click-order heuristics with focus-of-attention scan-paths generated from a computational model of visual attention. Our method results in a significantly better automated attack than previous work, guessing 8-15% of passwords for two representative images using dictionaries of less than 2^24.6 entries, and about 16% of passwords on each of these images using dictionaries of less than 2^31.4 entries (where the full password space is 2^43). Relaxing our click-order pattern substantially increased the efficacy of our attack albeit with larger dictionaries of 2^34.7 entries, allowing attacks that guessed 48-54% of passwords (compared to previous results of 0.9% and 9.1% on the same two images with 2^35 guesses). These latter automated attacks are independent of focus-of-attention models, and are based on image-independent guessing patterns. Our results show that automated attacks, which are easier to arrange than human-seeded attacks and are more scalable to systems that use multiple images, pose a significant threat.